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Record W4362640200 · doi:10.1159/000530366

Behavioral Changes after Psychiatric Genetic Counseling: An Exploratory Study

2023· article· en· W4362640200 on OpenAlexaff
Stephanie Huynh, Emily Morris, Angela Inglis, Jehannine Austin

Bibliographic record

VenuePublic Health Genomics · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShameMental illnessFeelingMental healthPsychologyPsychiatryExploratory researchClinical psychologyMedicineSocial psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Though it is well established that genetic information does not produce behavior changes, there are limited data regarding whether genetic counseling can facilitate changes in lifestyle and health behaviors that can result in improved health outcomes. METHODS: To explore this issue, we conducted semi-structured interviews with 8 patients who had lived experience of psychiatric illness and who had received psychiatric genetic counseling (PGC). Using interpretive description, we used a constant comparative approach to data analysis. RESULTS: Participants talked about how, prior to PGC, they held misconceptions and/or uncertainties about the causes of and protective behaviors associated with mental illness, which caused feelings of guilt, shame, fear, and hopelessness. Participants reported that PGC reframed things in a way that provided them a sense of agency over illness management, allowed a greater acceptance of illness, and provided release from some of the negative emotions associated with their initial framing of their illness, which seemed to be related to the self-reported increase in engagement in illness management behaviors and consequently improved mental health outcomes. CONCLUSION: This exploratory study provides evidence to support the idea that through addressing emotions associated with perceived cause of illness and facilitating understanding of etiology and risk-reducing strategies, PGC may lead to an increase in behaviors, which protect mental health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.064
GPT teacher head0.351
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2023
Admission routes1
Has abstractyes

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